The ML Interview Field Guide

Math, probability, classical machine learning, deep learning, and the core questions that test them.

Optimization, reliable experiments, implementation, debugging, and research judgment.

Metrics, experimental validity, calibration, product decisions, and production evaluation.

Transformer internals, inference, retrieval, evaluation, agents, alignment, and post-training.

Accelerators, distributed training, inference systems, reliability, cost, and full ML architecture.

Embeddings, candidate generation, ranking, search metrics, cold start, and feedback loops.

Sequential decisions, value and policy methods, environments, rewards, and robotics policy learning.

Visual models, multimodal systems, sequence modeling, natural language, and speech.

Role choice, level calibration, project stories, behavioral judgment, and long-form field guides.

A visual-first coding field guide for data structures, algorithms, and practical AI coding. Learn each problem by seeing the state it preserves, the move it makes, and the invariant that makes the move safe.